This paper describes a Bayesian graph matching algorithm for data-mining from large structural data-bases. The matching algorithm uses edge-consistency and node attribute similaritytodetermine the aposteriori probability of a query graph for eachofthe candidate matches in the data-base. The node feature-vectors are constructed by computing normalised histograms of pairwise geometric attributes. Attribute similarity is assessed by computing the Bhattacharyya distance between the histograms. Recognition is realised by selecting the candidate from the data-base which has the largest a posteriori probability. We illustrate the recognition technique on a data-base containing 2500 line patterns extracted from real-world imagery. Here the recognition technique is shown to significantly outperform a number of algorithm alternatives. 1 Introduction Since BarrowandPopplestone  first suggested that relational structures could be used to representandinterpret 2D scenes, there has ..
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